What is Natural Language Processing (NLP)?

Anwesha Roy

NLP has the potential to dramatically transform customer experiences

What is Natural Language Processing (NLP)?

Natural language processing or NLP has the potential to dramatically transform customer experiences. It would allow machines to read and understand natural languages like English and respond with a meaningful reply. It would enable automation at a previously unachievable level, allowing machines and humans to actually communicate.

In the last few years, advancements in artificial intelligence (AI) and NLP have paved the way towards newer use cases for human-machine interactions in CX management. From conversational interfaces to automated transcriptions of call recordings in a contact centre, there is an NLP technology powering most new-age CX systems.

What is NLP? Definition and Functionality

You can define natural language processing as a technology that allows humans and computers to interact, enabling computers to understand human beings, process and identify a response, and return this response in a form that is comprehensible to the human participant. Therefore, the functionality of an NLP engine can be segmented into three steps – understand, process, and respond. To achieve this, it utilises theories from the following disciplines:

  • Linguistics – How do different characters and character combinations form meaning? What are the modalities of meaning in different languages?
  • Artificial intelligence – How can previous interactions improve future understanding? What are the different options when it comes to choosing a response? How does the computer decide?
  • Computer science – How can systems be properly configured to accept human input? How do you optimise computing resources for consistent performance?

As you can see, NLP is a complex interdisciplinary area of study, often involving technologies like speech recognition and text analytics to uncover its full potential.

Why is NLP Crucial for CX Professionals?

CX management has always involved interacting with customers at scale and making sense of these communications, across multiple channels. NLP lets you convert this largely unstructured practice into a structured, formalised format – ready to pass through analytics, use as a trigger for automated events, etc. some of the key use cases for NLP in CX are:

  • Transcription tasks, like transcribing recorded calls in a contact centre or automated audio captioning in product tutorial videos
  • Customer feedback analysis by collecting unstructured, descriptive feedback to identify keywords, dominant sentiment, and trends
  • Paperless processing by extracting data from images, PDFs, and screenshots to populate electronic forms and fields (helpful for banking and the public sector)
  • CX personalisation by checking for specific keywords in written/telephonic communication and automatically triggering promo emails
  • Self-service and automated support using virtual customer service assistants powered by AI and NLP

The State of NLP Adoption

Despite its incredible potential, NLP is yet to become a CX staple due to two challenges – accuracy issues and computing demand. Human language is extremely nuanced, and it evolves every day. It is very difficult to pre-program an NLP library that can keep up with the dynamic evolution of how people communicate. Second, in order to store and process such vast amounts of data, you need substantial computing power. That’s why NLP budgets are growing slowly but steadily, with most companies spending 10% more in 2020 vs. 2019, according to a 2020 NLP survey report. There is still a lot of room for adoption, with plenty of use cases in the CX discipline.

Here are a few other key findings from the report:

  • Industry leaders and large companies with 5000+ employees are significantly ahead of the curve. In this segment, NLP budgets increased by 10-31% in one year
  • There are three popular NLP libraries in use (i.e., libraries of algorithms and datasets prebuilt for text recognition). More than half use Spark NLP and spaCy, while the former is used by nearly 1 in 3 companies
  • Interestingly, cloud-based NLP like the ones provided by Google, AWS, Azure, and IBM have been vital in driving adoption. 65% of companies are currently using at least one of these four cloud NLP solutions
  • Document classification, name recognition, sentiment analysis, and correlation mapping or knowledge graphs are four of the most popular applications of NLP today

Adopting NLP at Your Organisation

Typically, companies are held back by the lack of adequate in-house infrastructure and access to data science skills when it comes to NLP adoption. A single statement said in a natural language holds an incredible amount of data, from standalone keywords to sentence structure, from underlying sentiment to customer metadata. When you multiply this by thousands of customers speaking via tens of channels every day, there is a massive volume of data to parse.

That’s why most companies choose to partner with a specialised NLP company, with domain-specific expertise. You could, of course, leverage any of the open-source or commercial NLP libraries available to build your own solution – but this is a painstaking process. Some of the companies making significant strides in NLP for CX are:

  • Chattermill – An AI and NLP-powered feedback analysis solution, including CX automation, and sophisticated dashboards
  • Ascribe – A CX analysis and visualisation company, using Ai and NLP; also offers Ai project acceleration solutions
  • Wootric – a customer experience management and analytics software, including text and sentiment analytics powered by NLP

Apart from this, most major contact centre providers today like Genesys, Dialpad, and RingCentral incorporate NLP technology into their conversational offerings, making their chatbots more intuitive and accurate.

Here is a quick checklist to aid NLP solution assessment and selection for your organisation:

  1. Where do you need your NLP data to be hosted, on-premise, or on the cloud?
  2. What is the text-to-speech accuracy rate, and what are the measures taken to mitigate false positives?
  3. Are the NLP insights actionable – i.e., will the solution give you only a collection of transcriptions, real trending keywords, sentiment, and opinion, or prescribe an action based on the analysis?
  4. Can you customise the AI model based on your industry vocabulary and requirements?
  5. How does the NLP’s capability integrate with your existing CX solution stack – through ready APIs, or native integrations built from scratch? Does the feature ship with your largest solution bundle?

Fortunately, advancements in AI and the availability of open-source libraries gives you a world of opportunities for NLP deployment either from scratch or out of the box.



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